ESTIMATION OF PARAMETERS AND SELECTION OF MODELS APPLIED IN ADSORPTION

The modeling of complex phenomena such as adsorption often requires the determination of parameters that cannot be directly measured and, therefore, must be estimated. An important point is related to the analysis of the inverse problem as a method of estimating parameters and selecting models. In view of this, this work aims to apply the Monte Carlo method via Markov Chains (MCMC) as a technique for solving the inverse problem of estimating fixed-bed adsorption parameters using analytical models proposed in the literature. In addition, this work aims to select the best model through the statistical metrics Akaike, corrected Akaike and Bayesian Information Criterion. The use of the Bayesian approach allowed the analysis of the convergence of the chains, as well as selected the best model to represent the experimental data obtained from the literature.